April 13, 2012

It's been just over a year since I accepted a job as an analytics practitioner. At that point I'd been an analytics consultant in some form or another for 7 years, so it was a pretty dramatic move for me. The scope of my responsibility changed, too: I went from being a vice president at a very small company to being a director at a very large company. Plus I went from working at home to working in a high rise downtown. A lot has changed.

Over the past year, many of my analytics industry peers have asked me what it was like to switch teams. At this anniversary mark I'm finally able to reflect and share some observations.

1) A practitioner has an open-ended statement of work

Consultants develop and execute statements of work. Typically an entire project will get defined and agreed upon in advance - all the project milestones, all the deliverables. Because of this structure, I felt I had fairly clear blueprints for my work as a consultant.

As a practitioner, I applied for my position based on an initial job description, and then I continue to course-correct over time based on goals set jointly with my manager. That's not the same as a consulting statement of work, though. It's open-ended. I don't just produce the deliverable, collect payment and leave. The scope is adjustable and that's perfectly fine. I'm in this for the long haul.

2) What's the hold-up?

As a consultant, I used to wonder what took some of my enterprise clients so long to respond to my inquiries. I would hand something over for them to review and they would go silent, often for weeks.

You know what, I finally understand what goes on in that silent period. They're not just slacking. They're actually quite active within the walls of their company - talking to colleagues, getting buy-in, jumping through hoops. It can be frustrating. Things can take a long time. Some enterprises are more nimble than others, but we all have to deal with process, bureaocracy and red tape.

Please, please, consultants and vendors - please be aware that your clients aren't ignoring you. They're just navigating the waters of their company.

3) Meetings upon meetings upon meetings

When I started my job as a practitioner, my calendar was a blank slate. Then I started to get a few meetings. I happily accepted them - how flattering, people want to talk to me! Then I got a few more meetings. Then I started taking lunch meetings, and early morning meetings, and whole days worth of back-to-back half hour meetings - and then aaaaah, complete calendar gridlock!

As a consultant, a meeting meant that I was on billable time with a client, so I tried to make things as efficient and concise as possible. Practitioners don't necessarily have that same level of discipline. In the worst-case scenario, meetings can be incredibly wasteful and inefficient. If every meeting attendee billed hourly for the meetings they were expected to attend, perhaps we'd have fewer meetings (and the ones we do have would actually be fruitful).

Despite my distaste for meetings, though, I do realize that I have quite a few colleagues I need to communicate and coordinate with, and we do that by talking to each other - in meetings. If I was an individual contributor who did all of my work solo I might not need to attend too many meetings - but I'm not, so I don't. Rather, I'm a social person who does a lot of collaborative work and team work, and that type of activity requires meetings. I've learned to cope by blocking out periods of time on my calendar so I can concentrate and focus.

4) Longer projects, lasting impact

I used to suspect that practitioners brought in consultants to do their grunt work, and they kept the really interesting, impactful, important projects for internal teams. Now that I'm a practitioner, I know it's true. Sorry, consultants, you get the boring stuff. That's all I'm going to say.

5) Fear of losing touch

Something I truly loved about my role in consulting was the fact that I got to sample lots of different business models, and industry verticals, and toolsets, and corporate cultures. Now I am immersed - deeply, deeply immersed - in one place.

I really learned a lot from sampling that much diversity, and honestly I do miss it a bit. But my fear of losing touch with the analytics industry was, I believe, unfounded. I still have a good vantage point from which to view progress and innovation. It's a different view, though - one which I am courted by vendors and consultants rather than being treated as just another competitor.

With the breadth of expertise that I cultivated as a consultant, I can see past some of the marketing hype that comes my way as a practitioner, and I still feel as if I have a handle on where the industry is headed. Analytics practitoners are driving the adoption and use of techologies that will become commonplace in the future, so in that sense I am actually helping to lead the charge. How about that.

6) From center of the universe to subject matter expert

As a member of the leadership team at an analytics consultancy, analytics was my world. It was central to my company's business model. It was pretty much all we ever thought about, and talked about, and did.

As a practitioner, most of the people I interact with at work these days are not actually immersed in analytics. They come to my colleagues and me because we are the analytics subject matter experts. We are unique, rare, limited quantities. We understand the tools and the data and the trends of the business. We know what's going on because we have our fingers on the digital pulse of the customers.

I don't mind the fact that analysts aren't the center of the universe at my company. Analysts are respected for our knowledge and valued for our insights. We're not just bouncing around in the echo chamber of our own industry. We're contributing to something greater.

7) The role of a consultant is to make the client look good

When I was a consultant I felt that I filled the role of problem-solver. The client had a problem, they hired me, and I solved the problem. I now see it a bit differently. As a practitioner, I want my peers and superiors to think of me as a valued contributor. In short, I want to look good. I might hire a consultant to solve a problem, but really, my underlying motivation is that the consultant will help me accomplish something that will make me look good. It's that simple. And that vain.

Business rationalization of return on investment is still paramount to any consulting relationship. But now that I've filled both roles - consultant and client - I have a more thorough understanding of the forces that motivate consulting arrangements to take place and ultimately drive the perception of success on the part of the individuals who've entered into those agreements. Consultants: you are here to make your clients look good. Serve them well.

In closing

After a year's worth of hard work, I'm just beginning to see the rippling impact that my team has been able to make on the business. I've stopped feeling like a consultant masquerading as a practitioner. I've assimilated, but my roots as a consultant remain intact. I feel fortunate to understand the perspective of both roles, and I can indeed confirm that the grass is green on both sides of the fence.

January 02, 2012

Winter break is a time for slacking. I'll admit, I've been binging on peppermint bark and Words with Friends. Winter break is also a time when we all pause to contemplate the year ahead. Although I'm not usually prone to new year's resolutions, I do have a short list of things I'd like to work on, professionally, over the next 12 months:

1) Proactive, not reactive

It's all too easy for an analytics group to assume a help desk role within their company. You tell us what you need, and we serve it up. It's much harder (but much more valuable) to proactively approach business constituents with data they need - before they even know they need it. If an analytics practice is known solely as that-place-you-submit-a-ticket-and-get-a-spreadsheet-back, it's falling far short of its potential to serve the business.

2) Outward, not inward

"Siloed" is an overused term in the analytics industry, but it's an apt way to describe the way we typically feel. As analysts we often spend too much time talking amongst ourselves rather than communicating with - and collaborating with - others. In large part I think we bring it on ourselves, and it's within our power to overcome it. Over the next year I aim to meet people throughout my company who call themselves analysts; we may work in completely separate departments, but our common interest in data unites us.

3) Windshield, not rear-view

A common theme among analytics maturity models is the idea that the advanced stages rely heavily on predictive modeling rather than simply reporting on events that have already occurred. The motorist analogy sums it up nicely: as you drive a car, you look in the rear-view mirror to see where you've been (reporting); you look through the windshield to decide where to go next (predicting).

I'm not ashamed to say that, in my current work environment, we still need to work through some of the fundamentals before we realize the full potential of the predictive stage - but it helps to have the end goal in mind as we lay the foundation.

4) Share

Last March I made the switch from analytics consultant to analytics practitioner. It's been an incredible learning experience, but I haven't written or spoken too much about what I've been doing lately. Although my role does require a certain level of confidentiality, I would like to share more over the course of the next year. It's not just shop talk. I believe that, when we compare notes with our peers, we are driving innovation and carrying our industry forward as a whole.

October 25, 2011

Last week I attended the Tableau Customer Conference in Las Vegas. I'm not going to tell you what I did in Vegas (because, as they say, what happens in Vegas ....). Instead, I thought I'd share my conference notes. Fascinating.

If you fancy yourself a data analyst, you probably know and love Tableau. It's like Excel on steroids. I've used the product off and on since 2006; although I'm not a power user at the moment, there are some pockets of great expertise within my company.

Given my background, I attended the conference with two main objectives: 1) meet analysts who are doing "cool stuff" with data, and 2) figure out where Tableau fits in the broader business intelligence ecosystem. Here's what I found out.

Although the subject matter varied quite a bit from group to group, the common thread was a "do it yourself" approach to each initiative and an emphasis on self-service. Often the Tableau administrator was also the subject matter expert for the data at hand, and the output served their business group directly - no heavy process, no data warehousing middleman.

Tableau's position in the business intelligence ecosystem

Grassroots. Tableau usage definitely seems to be more "grassroots" than "institutional" - analysts love to get quick access to their data, but it does lack the structure that many of us are used to seeing within traditional data warehousing.

Decentralized. Deployment at some institutions is totally decentralized – groups will take it and use it when they have a need, as opposed to big, centrally-managed BI initiatives.

Versus other BI tools. Tableau has not totally replaced tools like Microstrategy in most institutions. There's room for both types of tools. Some consider Tableau as a prototyping tool for dashboards that are later built in Microstrategy. I ran into a former colleague who serves as a Microstrategy administrator at a very large company. And yet, there he was at the Tableau conference. No irony. He sees them as complimentary products.

Presentation. Some practitioners say that Tableau is actually changing the way that they interact with their executives – less static Powerpoint, more exploratory data sharing sessions.

Beauty and the Beast. Tableau can produce some beautiful output, but there's no accounting for taste. If you create heinously ugly Excel charts, you will probably create only slightly less ugly Tableau output. Believe me, I saw it.

Again, grassroots. I saw numerous examples of individual contributors leading the way, rather than centralized BI/DW groups. Broader corporate socialization about Tableau often bubbled up from groups that demonstrated success.

Guy Kawasaki – broadly appealing, though his content was certainly not tailored to the subject of the conference. Made a number of references to "Mac vs PC" debate, but perhaps not realizing that Tableau runs on PC only.

Cory Doctorow – spoke on data privacy. Philosophy a bit at odds with analysts in the audience, but worth surfacing as valid concern.

Stephen Few - whose talk I missed, but whose contributions to the industry I respect a great deal.

Some technical tidbits

Hadoop support – new in Tableau 7. As I see it, it's just another data source, but connection allows for some control over data modeling

Google Analytics support – no direct connection available, must use an intermediate layer like Analytics Canvas

March 10, 2011

Throughout my career I’ve always enjoyed counseling fellow web analysts on professional development. Everyone wrestles with questions like: How can I get my start? What can I do to ensure positive momentum? Which opportunity would suit me best? Although I’ve helped so many of my peers navigate job changes, it’s always a humbling experience when I go through it for myself.

I’ve left Semphonic

Earlier this month I resigned from my position on the leadership team at Semphonic. Looking back over the past 3 ½ years, I feel very fortunate to have worked with such wonderful colleagues, clients and partners. I leave with a wealth of experience and good memories. Thank you, everyone.

I’ve joined Apollo Group

After 7 years in consulting, I’m excited to say that I’m returning to the client side. I’ve accepted a position as Director of Web Analytics and Customer Insight at Apollo Group, the parent company of University of Phoenix. My work will support the online learning platform team, which is based downtown San Francisco.

In this new role I look forward to all of the challenges that come along with building and managing a thriving web analytics practice. I also get the satisfaction of knowing that my work will improve the educational experience for hundreds of thousands of students, alumni and faculty members. I’m thrilled.

I will remain involved in the community

Several people have asked if I’ll continue to serve on the Board of Directors of the Web Analytics Association after my job transition. The answer is absolutely yes, I’ll stay active in the WAA. I will also keep blogging and tweeting about the work I do, and of course you can count on seeing me at Web Analytics Wednesday.

January 07, 2011

I was recently one of several people interviewed for an Inc.com story about Web analytics tools for small business. My fellow contributors included industry thought leaders such as Bryan Eisenberg and Eric Peterson. The resulting article provided an excellent summary of our collective opinion. I’ve chosen to publish my personal top 10 list here so you can see the full range of my own recommendations.

Taken together, the tools on my personal list cover the full palette of Web analytics: quantitative, qualitative, optimization and competitive analysis. I've also thrown in a couple of tools you can use to measure emerging media such as social and mobile.

Every tool on my list is either low-cost or free. Think of them as starter tools for each type of measurement category.

If you’re having trouble getting funding for expensive-but-important tools, try this approach: use low-cost tools to showcase some early successes with limited budget, then win over management and use that as leverage to get funding. (Credit goes to Avinash Kaushik on this clever solution to the budget issue.)

Without further ado, here are my top picks for broad-coverage, low-cost, high-impact analytics tools for small business.

Category: Quantitative

1) Google Analytics See where your visitors come from, what they do on your site, and how often they come back. Setting up goals allows you to perform the essential analysis function of tying behavior to outcomes.

2) Crazy Egg Generate heatmaps that show you exactly where your visitors are clicking.

7) Bitly Shorten and share URLs through social media platforms, then track traffic volume over time. Adding a "+" to the end of any bitly URL will allow you to see stats for that link - so you can use it as a competitive tool, too.

9) Percent Mobile Simple tool that allows you to see the percent of visitors to your Web site who use mobile devices.

Bonus Addition

10) Brain Power You won't get anywhere with these tools unless you actually have someone to use them and interpret results - so make sure you have a dedicated analyst who can feed recommendations back to your business.

October 04, 2010

It’s not difficult to track social media marketing efforts as campaigns, but I haven’t seen too many companies actually doing it yet. Before I lay out step-by-step instructions, here’s a story that gives me hope for the future:

At last month’s X Change conference I sat in on a very popular social media analytics discussion; there were about 20 Web analytics practitioners in the room from a variety of large enterprises. At a certain point our conversation turned toward measuring ROI. The room grew quiet except for one voice.

“I can tell you exactly how well social media is working for us,” said an individual - who shall remain nameless - representing a major consumer brand. He pulled up a Web analytics report on his smartphone and stated, “Here’s a Twitter campaign we ran last month that generated $23,000 in revenue.”

He was able to make this claim precisely because his company tracks their social media links as campaigns. If you want similar bragging rights for your own company, just follow this 4-step process:

1. Tag

First, append campaign codes to the URL you plan to post on social media platforms. Follow your company’s campaign coding standards if an established policy exists. Treat social media just as you would more traditional channels like email and banners.

If you’re planning to post on multiple platforms, I recommend creating one campaign code for each platform. For instance, make a unique code for Twitter, a unique code for Facebook, a unique code for LinkedIn, and so on for each intended destination.

2. Shrink

Short links are easier to share, so pass each of your tagged links through a URL shortener like bit.ly or goo.gl. There’s also an opportunity to collect stats at this step. See my related blog post on URL shorteners with analytics.

3. Post

Once you have a set of tagged short links, go out and post them on Twitter, Facebook, LinkedIn, etc.

Although these first 3 steps may seem tedious, there are some opportunities for automation. For instance, if you use Hootsuite you can tag, shrink and post within a single interface, since campaign coding and URL shortening are built directly into the tool. I’ve also seen companies build simple tools from scratch that allow them to automate tagging and shrinking their URLs, they then grab the links and post them manually. The choice is up to you; just find a process your team can live with.

4. Analyze

Now sit back and wait for data. By default your Web analytics tool will give you visit volume for each of the campaign codes that you’ve used. Beyond that, assuming you’re tracking your site’s goal behaviors - purchases, downloads, form submissions, video views – you’ll be able to see the downstream impact of your social campaigns.

In the story I told at the beginning of this post, the company uses their Web analytics tool to track purchases on their commerce site, so they’re able to connect the dots between campaigns to revenue.

A final note on URL shorteners: Since it’s possible to get basic clickthrough stats from popular URL shorteners like bit.ly, some people may question whether it’s necessary to append campaign codes at all. Here’s the clincher: URL shorteners track clicks but they do not tie to downstream goal behaviors. If you’re serious about connecting social media efforts to outcomes, you must track links as campaigns.

September 17, 2010

The phrase "web analytics salary" hasn't been uttered on this blog in nearly 3 years, and yet my post on that topic remains one of the all-time most popular pieces of content I've written. I see it as a testament to just how hungry Web analytics professionals are for information that will help us determine fair compensation for ourselves and the people we hire.

If you are on a quest for salary information, you must read this great new piece of research from Corry Prohens at IQ Workforce: Web Analytics Salary Guide.

Corry's explanations and disclaimers are required reading; you cannot simply grab a cell value and treat it as the gospel. Instead, think about what makes your situation unique and develop a realistic range for yourself based on broader research: other salary studies, job board info, personal conversations, etc.

August 23, 2010

Suppose you’re planning to run a simple A/B test on a Web page and you’d like to use bounce rate as your measure of success. Can you do it with Google Website Optimizer (GWO)?

I’ve had this issue crop up a number of times recently, so I decided to research it. Here’s what I found out.

Can I set bounce rate as a conversion goal within GWO?

No. GWO itself does not report bounce rate, nor does it allow bounce rate to be set as a conversion goal. It’s possible to approximate bounce rate as a goal by coding every single link click on every single experiment page as a conversion event, but that gives you exit rate, not bounce rate - plus the setup can take a lot of effort.

Can I get bounce rate for GWO A/B test variations from Google Analytics or Omniture?

Yes. Although bounce rate isn’t reported within GWO, you can get it from Web analytics tools such as Google Analytics and Omniture. Integrating the data turns out to be very easy, and in fact requires zero coding as long as all experiment pages contain your standard Web analytics page tags.

Here’s why it’s easy: As GWO serves variations in an A/B test, visitors are redirected to unique URLs (indexA.html vs. indexB.html vs. indexC.html, for example). This behavior was contrary to my initial assumption that all visitors got the same URL, as is the case for GWO multivariate tests. Since URLs are unique in an A/B test, you can simply view the pages report within your Web analytics tool and filter on (for example) index*.html to see one row per variation.

Do I need to pass any custom variables from GWO to my Web analytics tool?

No. You don’t need to pass any custom variables to get reporting for an A/B test.

If you're running a multivariate test, on the other hand, you must pass the GWO variation ID to your Web analytics tool as a custom variable, since the URL remains the same for all variations. If you’re interested in this technique, there are good discussion threads on this topic in the GWO support forum.

If I optimize for bounce rate outside of GWO, do I still need to set up a goal within GWO?

Yes, but it can be a simple placeholder. When you create a GWO A/B test you’re required to provide a conversion goal before you can launch. However, if you're planning to optimize for bounce rate or some other metric in your standard Web analytics tool, you can spoof a GWO conversion goal by specifying a dummy page somewhere on your site. In effect, you're simply using GWO to manage the serving of page variations.

To prove that it can be done, here are some screen shots from a test I set up:

June 30, 2010

Sometimes I need to compare 2 lists of URLs and find out how much they overlap. Which URLs are only in list #1? Which URLs are in list #2? Which URLs are in both lists?

Here’s an example: say I’ve got one list of URLs from my Web analytics tool (like Content>Top Content in Google Analytics) and another list of URLs from a sitemap-generating spider. Comparing the 2 lists lets me see which pages are actually getting traffic on my site versus which pages are reachable by spider. Although most URLs that get traffic are also spiderable, it’s also likely that some URLs get traffic but aren’t spiderable (like thank you pages) and some URLs are spiderable but get zero traffic.

I've developed a quick and easy way to do this kind of comparison using Excel. You can download it here. I’ve included step-by-step instructions for you to follow. There are 8 steps:

If you're a programmer I’m sure you have a fancier way to compare lists. However, the beauty of my method is that anyone can do it as long as they have a copy of Excel and a basic understanding of pivot tables.

Speaking of pivot tables, if you've never created one I encourage you to learn right this minute. It's something that every single Web analyst should know how to do. If I ever interview you for an analyst job, even an entry-level one, I will quiz you on pivot tables.

What do you think? Do you have an even easier method? And, as a Web analyst, why have you needed to compare lists of URLs?